


'''simple docstring'''


import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

				if is_vision_available():
								from transformers import OneFormerImageProcessor
								from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
								from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
				from PIL import Image







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__="shi-labs/oneformer_demo"	):







				"""simple docstring"""





				with open(hf_hub_download(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, repo_type="""dataset"""	)				, """r"""	) as f:
								_SCREAMING_SNAKE_CASE					:			List[str]							= json.load(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= {}
				_SCREAMING_SNAKE_CASE					:			str							= []
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= []
				for key, info in class_info.items():
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= info["""name"""]
								class_names.append(info["""name"""]	)
								if info["isthing"]:
												thing_ids.append(int(SCREAMING_SNAKE_CASE__	)	)
				_SCREAMING_SNAKE_CASE					:			Any							= thing_ids
				_SCREAMING_SNAKE_CASE					:			int							= class_names
				return metadata





class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							def __init__(       self       ,   __snake_case       ,   __snake_case=7       ,   __snake_case=3       ,   __snake_case=30       ,   __snake_case=400       ,   __snake_case=None       ,   __snake_case=True       ,   __snake_case=True       ,   __snake_case=[0.5, 0.5, 0.5]       ,   __snake_case=[0.5, 0.5, 0.5]       ,   __snake_case=10       ,   __snake_case=False       ,   __snake_case=255       ,   __snake_case="shi-labs/oneformer_demo"       ,   __snake_case="ade20k_panoptic.json"       ,   __snake_case=10       ,   ):
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= parent
											_SCREAMING_SNAKE_CASE					:			Any							= batch_size
											_SCREAMING_SNAKE_CASE					:			List[str]							= num_channels
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= min_resolution
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= max_resolution
											_SCREAMING_SNAKE_CASE					:			List[str]							= do_resize
											_SCREAMING_SNAKE_CASE					:			Dict							= {"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
											_SCREAMING_SNAKE_CASE					:			Any							= do_normalize
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= image_mean
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= image_std
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= class_info_file
											_SCREAMING_SNAKE_CASE					:			Dict							= prepare_metadata(__snake_case       ,   __snake_case    )
											_SCREAMING_SNAKE_CASE					:			str							= num_text
											_SCREAMING_SNAKE_CASE					:			Dict							= repo_path

											# for the post_process_functions
											_SCREAMING_SNAKE_CASE					:			List[str]							= 2
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= 10
											_SCREAMING_SNAKE_CASE					:			Tuple							= 10
											_SCREAMING_SNAKE_CASE					:			str							= 3
											_SCREAMING_SNAKE_CASE					:			Any							= 4
											_SCREAMING_SNAKE_CASE					:			List[Any]							= num_labels
											_SCREAMING_SNAKE_CASE					:			Tuple							= do_reduce_labels
											_SCREAMING_SNAKE_CASE					:			List[Any]							= ignore_index






							def UpperCAmelCase_      (       self    ):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "num_labels": self.num_labels,
											    "do_reduce_labels": self.do_reduce_labels,
											    "ignore_index": self.ignore_index,
											    "class_info_file": self.class_info_file,
											    "metadata": self.metadata,
											    "num_text": self.num_text,
											}






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case=False    ):
											if not batched:
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= image_inputs[0]
															if isinstance(__snake_case       ,   Image.Image    ):
																			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= image.size
															else:
																			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= image.shape[1], image.shape[2]
															if w < h:
																			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= int(self.size["""shortest_edge"""] * h / w    )
																			_SCREAMING_SNAKE_CASE					:			str							= self.size["""shortest_edge"""]
															elif w > h:
																			_SCREAMING_SNAKE_CASE					:			int							= self.size["""shortest_edge"""]
																			_SCREAMING_SNAKE_CASE					:			List[Any]							= int(self.size["""shortest_edge"""] * w / h    )
															else:
																			_SCREAMING_SNAKE_CASE					:			int							= self.size["""shortest_edge"""]
																			_SCREAMING_SNAKE_CASE					:			Dict							= self.size["""shortest_edge"""]

											else:
															_SCREAMING_SNAKE_CASE					:			List[str]							= []
															for image in image_inputs:
																			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Dict							= self.get_expected_values([image]    )
																			expected_values.append((expected_height, expected_width)    )
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= max(__snake_case       ,   key=lambda __snake_case    : item[0]    )[0]
															_SCREAMING_SNAKE_CASE					:			str							= max(__snake_case       ,   key=lambda __snake_case    : item[1]    )[1]

											return expected_height, expected_width






							def UpperCAmelCase_      (       self    ):
											return OneFormerForUniversalSegmentationOutput(
											    # +1 for null class
											    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)    )       ,   masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)    )       ,   )





@require_torch
@require_vision
class        lowercase__    (		_snake_case	,	unittest.TestCase   ):





							'''simple docstring'''


							A_				: Union[str, Any]       =    OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
							# only for test_image_processing_common.test_image_proc_to_json_string
							A_				: Union[str, Any]       =    image_processing_class






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= OneFormerImageProcessorTester(self    )






							@property
							def UpperCAmelCase_      (       self    ):
											return self.image_processing_tester.prepare_image_processor_dict()






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Dict							= self.image_processing_class(**self.image_processor_dict    )
											self.assertTrue(hasattr(__snake_case       ,   """image_mean"""    )    )
											self.assertTrue(hasattr(__snake_case       ,   """image_std"""    )    )
											self.assertTrue(hasattr(__snake_case       ,   """do_normalize"""    )    )
											self.assertTrue(hasattr(__snake_case       ,   """do_resize"""    )    )
											self.assertTrue(hasattr(__snake_case       ,   """size"""    )    )
											self.assertTrue(hasattr(__snake_case       ,   """ignore_index"""    )    )
											self.assertTrue(hasattr(__snake_case       ,   """class_info_file"""    )    )
											self.assertTrue(hasattr(__snake_case       ,   """num_text"""    )    )
											self.assertTrue(hasattr(__snake_case       ,   """repo_path"""    )    )
											self.assertTrue(hasattr(__snake_case       ,   """metadata"""    )    )
											self.assertTrue(hasattr(__snake_case       ,   """do_reduce_labels"""    )    )






							def UpperCAmelCase_      (       self    ):
											pass






							def UpperCAmelCase_      (       self    ):
											# Initialize image_processor
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self.image_processing_class(**self.image_processor_dict    )
											# create random PIL images
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= prepare_image_inputs(self.image_processing_tester       ,   equal_resolution=__snake_case    )
											for image in image_inputs:
															self.assertIsInstance(__snake_case       ,   Image.Image    )

											# Test not batched input
											_SCREAMING_SNAKE_CASE					:			int							= image_processor(image_inputs[0]       ,   ["""semantic"""]       ,   return_tensors="""pt"""    ).pixel_values

											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			int							= self.image_processing_tester.get_expected_values(__snake_case    )

											self.assertEqual(
											    encoded_images.shape       ,   (1, self.image_processing_tester.num_channels, expected_height, expected_width)       ,   )

											# Test batched
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= self.image_processing_tester.get_expected_values(__snake_case       ,   batched=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			List[Any]							= image_processor(
											    __snake_case       ,   ["""semantic"""] * len(__snake_case    )       ,   return_tensors="""pt"""    ).pixel_values
											self.assertEqual(
											    encoded_images.shape       ,   (
											        self.image_processing_tester.batch_size,
											        self.image_processing_tester.num_channels,
											        expected_height,
											        expected_width,
											    )       ,   )






							def UpperCAmelCase_      (       self    ):
											# Initialize image_processor
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.image_processing_class(**self.image_processor_dict    )
											# create random numpy tensors
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= prepare_image_inputs(self.image_processing_tester       ,   equal_resolution=__snake_case       ,   numpify=__snake_case    )
											for image in image_inputs:
															self.assertIsInstance(__snake_case       ,   np.ndarray    )

											# Test not batched input
											_SCREAMING_SNAKE_CASE					:			Dict							= image_processor(image_inputs[0]       ,   ["""semantic"""]       ,   return_tensors="""pt"""    ).pixel_values

											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self.image_processing_tester.get_expected_values(__snake_case    )

											self.assertEqual(
											    encoded_images.shape       ,   (1, self.image_processing_tester.num_channels, expected_height, expected_width)       ,   )

											# Test batched
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self.image_processing_tester.get_expected_values(__snake_case       ,   batched=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			int							= image_processor(
											    __snake_case       ,   ["""semantic"""] * len(__snake_case    )       ,   return_tensors="""pt"""    ).pixel_values
											self.assertEqual(
											    encoded_images.shape       ,   (
											        self.image_processing_tester.batch_size,
											        self.image_processing_tester.num_channels,
											        expected_height,
											        expected_width,
											    )       ,   )






							def UpperCAmelCase_      (       self    ):
											# Initialize image_processor
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.image_processing_class(**self.image_processor_dict    )
											# create random PyTorch tensors
											_SCREAMING_SNAKE_CASE					:			List[Any]							= prepare_image_inputs(self.image_processing_tester       ,   equal_resolution=__snake_case       ,   torchify=__snake_case    )
											for image in image_inputs:
															self.assertIsInstance(__snake_case       ,   torch.Tensor    )

											# Test not batched input
											_SCREAMING_SNAKE_CASE					:			Dict							= image_processor(image_inputs[0]       ,   ["""semantic"""]       ,   return_tensors="""pt"""    ).pixel_values

											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= self.image_processing_tester.get_expected_values(__snake_case    )

											self.assertEqual(
											    encoded_images.shape       ,   (1, self.image_processing_tester.num_channels, expected_height, expected_width)       ,   )

											# Test batched
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= self.image_processing_tester.get_expected_values(__snake_case       ,   batched=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= image_processor(
											    __snake_case       ,   ["""semantic"""] * len(__snake_case    )       ,   return_tensors="""pt"""    ).pixel_values
											self.assertEqual(
											    encoded_images.shape       ,   (
											        self.image_processing_tester.batch_size,
											        self.image_processing_tester.num_channels,
											        expected_height,
											        expected_width,
											    )       ,   )






							def UpperCAmelCase_      (       self       ,   __snake_case=False       ,   __snake_case=False       ,   __snake_case="np"    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self.image_processing_class(**self.image_processor_dict    )
											# prepare image and target
											_SCREAMING_SNAKE_CASE					:			str							= self.image_processing_tester.num_labels
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= None
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= None
											_SCREAMING_SNAKE_CASE					:			Dict							= prepare_image_inputs(self.image_processing_tester       ,   equal_resolution=__snake_case    )
											if with_segmentation_maps:
															_SCREAMING_SNAKE_CASE					:			int							= num_labels
															if is_instance_map:
																			_SCREAMING_SNAKE_CASE					:			Tuple							= list(range(__snake_case    )    ) * 2
																			_SCREAMING_SNAKE_CASE					:			Tuple							= dict(enumerate(__snake_case    )    )
															_SCREAMING_SNAKE_CASE					:			List[Any]							= [
															    np.random.randint(0       ,   high * 2       ,   (img.size[1], img.size[0])    ).astype(np.uinta    ) for img in image_inputs
															]
															if segmentation_type == "pil":
																			_SCREAMING_SNAKE_CASE					:			int							= [Image.fromarray(__snake_case    ) for annotation in annotations]

											_SCREAMING_SNAKE_CASE					:			int							= image_processor(
											    __snake_case       ,   ["""semantic"""] * len(__snake_case    )       ,   __snake_case       ,   return_tensors="""pt"""       ,   instance_id_to_semantic_id=__snake_case       ,   pad_and_return_pixel_mask=__snake_case       ,   )

											return inputs






							def UpperCAmelCase_      (       self    ):
											pass






							def UpperCAmelCase_      (       self    ):
											def common(__snake_case=False       ,   __snake_case=None    ):
															_SCREAMING_SNAKE_CASE					:			str							= self.comm_get_image_processor_inputs(
															    with_segmentation_maps=__snake_case       ,   is_instance_map=__snake_case       ,   segmentation_type=__snake_case    )

															_SCREAMING_SNAKE_CASE					:			List[Any]							= inputs["""mask_labels"""]
															_SCREAMING_SNAKE_CASE					:			Tuple							= inputs["""class_labels"""]
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= inputs["""pixel_values"""]
															_SCREAMING_SNAKE_CASE					:			List[str]							= inputs["""text_inputs"""]

															# check the batch_size
															for mask_label, class_label, text_input in zip(__snake_case       ,   __snake_case       ,   __snake_case    ):
																			self.assertEqual(mask_label.shape[0]       ,   class_label.shape[0]    )
																			# this ensure padding has happened
																			self.assertEqual(mask_label.shape[1:]       ,   pixel_values.shape[2:]    )
																			self.assertEqual(len(__snake_case    )       ,   self.image_processing_tester.num_text    )

											common()
											common(is_instance_map=__snake_case    )
											common(is_instance_map=__snake_case       ,   segmentation_type="""pil"""    )
											common(is_instance_map=__snake_case       ,   segmentation_type="""pil"""    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Dict							= np.zeros((20, 50)    )
											_SCREAMING_SNAKE_CASE					:			Any							= 1
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= 1
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 1

											_SCREAMING_SNAKE_CASE					:			str							= binary_mask_to_rle(__snake_case    )
											self.assertEqual(len(__snake_case    )       ,   4    )
											self.assertEqual(rle[0]       ,   21    )
											self.assertEqual(rle[1]       ,   45    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			str							= self.image_processing_class(
											    num_labels=self.image_processing_tester.num_classes       ,   max_seq_length=77       ,   task_seq_length=77       ,   class_info_file="""ade20k_panoptic.json"""       ,   num_text=self.image_processing_tester.num_text       ,   repo_path="""shi-labs/oneformer_demo"""       ,   )
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.image_processing_tester.get_fake_oneformer_outputs()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= fature_extractor.post_process_semantic_segmentation(__snake_case    )

											self.assertEqual(len(__snake_case    )       ,   self.image_processing_tester.batch_size    )
											self.assertEqual(
											    segmentation[0].shape       ,   (
											        self.image_processing_tester.height,
											        self.image_processing_tester.width,
											    )       ,   )

											_SCREAMING_SNAKE_CASE					:			int							= [(1, 4) for i in range(self.image_processing_tester.batch_size    )]
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= fature_extractor.post_process_semantic_segmentation(__snake_case       ,   target_sizes=__snake_case    )

											self.assertEqual(segmentation[0].shape       ,   target_sizes[0]    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			int							= self.image_processing_class(
											    num_labels=self.image_processing_tester.num_classes       ,   max_seq_length=77       ,   task_seq_length=77       ,   class_info_file="""ade20k_panoptic.json"""       ,   num_text=self.image_processing_tester.num_text       ,   repo_path="""shi-labs/oneformer_demo"""       ,   )
											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.image_processing_tester.get_fake_oneformer_outputs()
											_SCREAMING_SNAKE_CASE					:			str							= image_processor.post_process_instance_segmentation(__snake_case       ,   threshold=0    )

											self.assertTrue(len(__snake_case    ) == self.image_processing_tester.batch_size    )
											for el in segmentation:
															self.assertTrue("""segmentation""" in el    )
															self.assertTrue("""segments_info""" in el    )
															self.assertEqual(type(el["""segments_info"""]    )       ,   __snake_case    )
															self.assertEqual(
															    el["""segmentation"""].shape       ,   (self.image_processing_tester.height, self.image_processing_tester.width)    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.image_processing_class(
											    num_labels=self.image_processing_tester.num_classes       ,   max_seq_length=77       ,   task_seq_length=77       ,   class_info_file="""ade20k_panoptic.json"""       ,   num_text=self.image_processing_tester.num_text       ,   repo_path="""shi-labs/oneformer_demo"""       ,   )
											_SCREAMING_SNAKE_CASE					:			Dict							= self.image_processing_tester.get_fake_oneformer_outputs()
											_SCREAMING_SNAKE_CASE					:			Tuple							= image_processor.post_process_panoptic_segmentation(__snake_case       ,   threshold=0    )

											self.assertTrue(len(__snake_case    ) == self.image_processing_tester.batch_size    )
											for el in segmentation:
															self.assertTrue("""segmentation""" in el    )
															self.assertTrue("""segments_info""" in el    )
															self.assertEqual(type(el["""segments_info"""]    )       ,   __snake_case    )
															self.assertEqual(
															    el["""segmentation"""].shape       ,   (self.image_processing_tester.height, self.image_processing_tester.width)    )








'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase_						:  int				  =						logging.get_logger(__name__)


if is_vision_available():
				import PIL





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: int       =    ["""pixel_values"""]






							def __init__(       self       ,   __snake_case = True       ,   __snake_case = None       ,   __snake_case = PILImageResampling.BICUBIC       ,   __snake_case = True       ,   __snake_case = None       ,   __snake_case = True       ,   __snake_case = 1 / 255       ,   __snake_case = True       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = True       ,   **__snake_case       ,   ):
											super().__init__(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			int							= size if size is not None else {"""shortest_edge""": 224}
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= get_size_dict(__snake_case       ,   default_to_square=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
											_SCREAMING_SNAKE_CASE					:			Any							= get_size_dict(__snake_case       ,   default_to_square=__snake_case       ,   param_name="""crop_size"""    )

											_SCREAMING_SNAKE_CASE					:			str							= do_resize
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= size
											_SCREAMING_SNAKE_CASE					:			List[str]							= resample
											_SCREAMING_SNAKE_CASE					:			str							= do_center_crop
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= crop_size
											_SCREAMING_SNAKE_CASE					:			Tuple							= do_rescale
											_SCREAMING_SNAKE_CASE					:			Dict							= rescale_factor
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= do_normalize
											_SCREAMING_SNAKE_CASE					:			List[Any]							= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= image_std if image_std is not None else OPENAI_CLIP_STD
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= do_convert_rgb






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case = PILImageResampling.BICUBIC       ,   __snake_case = None       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			int							= get_size_dict(__snake_case       ,   default_to_square=__snake_case    )
											if "shortest_edge" not in size:
															raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
											_SCREAMING_SNAKE_CASE					:			Any							= get_resize_output_image_size(__snake_case       ,   size=size["""shortest_edge"""]       ,   default_to_square=__snake_case    )
											return resize(__snake_case       ,   size=__snake_case       ,   resample=__snake_case       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case = None       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= get_size_dict(__snake_case    )
											if "height" not in size or "width" not in size:
															raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""    )
											return center_crop(__snake_case       ,   size=(size["""height"""], size["""width"""])       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case = None       ,   **__snake_case       ,   ):
											return rescale(__snake_case       ,   scale=__snake_case       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case       ,   __snake_case = None       ,   **__snake_case       ,   ):
											return normalize(__snake_case       ,   mean=__snake_case       ,   std=__snake_case       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = ChannelDimension.FIRST       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= do_resize if do_resize is not None else self.do_resize
											_SCREAMING_SNAKE_CASE					:			Any							= size if size is not None else self.size
											_SCREAMING_SNAKE_CASE					:			List[str]							= get_size_dict(__snake_case       ,   param_name="""size"""       ,   default_to_square=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Any							= resample if resample is not None else self.resample
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= do_center_crop if do_center_crop is not None else self.do_center_crop
											_SCREAMING_SNAKE_CASE					:			str							= crop_size if crop_size is not None else self.crop_size
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= get_size_dict(__snake_case       ,   param_name="""crop_size"""       ,   default_to_square=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= do_rescale if do_rescale is not None else self.do_rescale
											_SCREAMING_SNAKE_CASE					:			int							= rescale_factor if rescale_factor is not None else self.rescale_factor
											_SCREAMING_SNAKE_CASE					:			Tuple							= do_normalize if do_normalize is not None else self.do_normalize
											_SCREAMING_SNAKE_CASE					:			List[str]							= image_mean if image_mean is not None else self.image_mean
											_SCREAMING_SNAKE_CASE					:			List[Any]							= image_std if image_std is not None else self.image_std
											_SCREAMING_SNAKE_CASE					:			Any							= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= make_list_of_images(__snake_case    )

											if not valid_images(__snake_case    ):
															raise ValueError(
															    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
															    """torch.Tensor, tf.Tensor or jax.ndarray."""    )

											if do_resize and size is None:
															raise ValueError("""Size must be specified if do_resize is True."""    )

											if do_center_crop and crop_size is None:
															raise ValueError("""Crop size must be specified if do_center_crop is True."""    )

											if do_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""    )

											if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("""Image mean and std must be specified if do_normalize is True."""    )

											# PIL RGBA images are converted to RGB
											if do_convert_rgb:
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= [convert_to_rgb(__snake_case    ) for image in images]

											# All transformations expect numpy arrays.
											_SCREAMING_SNAKE_CASE					:			str							= [to_numpy_array(__snake_case    ) for image in images]

											if do_resize:
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= [self.resize(image=__snake_case       ,   size=__snake_case       ,   resample=__snake_case    ) for image in images]

											if do_center_crop:
															_SCREAMING_SNAKE_CASE					:			Tuple							= [self.center_crop(image=__snake_case       ,   size=__snake_case    ) for image in images]

											if do_rescale:
															_SCREAMING_SNAKE_CASE					:			List[Any]							= [self.rescale(image=__snake_case       ,   scale=__snake_case    ) for image in images]

											if do_normalize:
															_SCREAMING_SNAKE_CASE					:			Dict							= [self.normalize(image=__snake_case       ,   mean=__snake_case       ,   std=__snake_case    ) for image in images]

											_SCREAMING_SNAKE_CASE					:			str							= [to_channel_dimension_format(__snake_case       ,   __snake_case    ) for image in images]

											_SCREAMING_SNAKE_CASE					:			int							= {"""pixel_values""": images}
											return BatchFeature(data=__snake_case       ,   tensor_type=__snake_case    )




